I started this project as usual with ESPN’s play-by-play data… but after I’ve finished and compared to aforementioned sources I discovered a shocking fact: ESPN’s version has a nasty habit of changing dunks into 1-foot shots! And it’s not a small issue either – we are talking about difference of around 3000 plays per year! So those files landed in trash along mine time spent on it and I had to start from scratch.

For my second attempt I used play-by-play data from basketballvalue.com… and even though their numbers looked fine it wasn’t a smooth ride either because of another nasty habit – they identify players only by surnames… but in some games they added a first letter to make a distinction between players on different teams. Such situation creates a litany of possible identification conflicts, for example just note how many players in the NBA are named Williams or Johnson, but through battery of tests and corrections by hand I think I sorted it all out successfully.
With that said keep in mind that there could be a couple of mistakes here, especially with players who played little or those who had the same name on the same team, but overall I think it should be accurate.

I started this project as usual with ESPN’s play-by-play data… but after I’ve finished and compared to aforementioned sources I discovered a shocking fact: ESPN’s version has a nasty habit of changing dunks into 1-foot shots! And it’s not a small issue either – we are talking about difference of around 3000 plays per year! So those files landed in trash along mine time spent on it and I had to start from scratch.

For my second attempt I used play-by-play data from basketballvalue.com… and even though their numbers looked fine it wasn’t a smooth ride either because of another nasty habit – they identify players only by surnames… but in some games they added a first letter to make a distinction between players on different teams. Such situation creates a litany of possible identification conflicts, for example just note how many players in the NBA are named Williams or Johnson, but through battery of tests and corrections by hand I think I sorted it all out successfully.
With that said keep in mind that there could be a couple of mistakes here, especially with players who played little or those who had the same name on the same team, but overall I think it should be accurate.

In my second piece about incentives to lose in the NBA I’ll focus mainly on extremes. Because even though it is a well known strategy I don’t think there is any research on… pointing fingers to actual offenders!
In other words, are there teams which tank more often than others?
Or is it simply year-by-year case-by-case strategy?

The data, timeframe and [simple] methodology used is exactly the same as in my previous post about tanking so I won’t repeat myself here and I’ll go straight into IMHO interesting curiosity…

# Worst Record

Total Difference

Biggest Minus

By Team

During Season

1

6

-9

Blazers

2005-06

2

-19

-9

Warriors

2000-01

3

-14

-6

Bucks

2006-07

4

-12

-10

Nuggets

1991-92

5

-32

-8

Clippers

2003-04

6

-10

-9

Sixers

1993-94

7

-35

-10

Cavs

2000-01

8

-3

-9

Clippers

2009-10

9

-7

-10

Lakers

2004-05

10

29

-7

Bucks

1996-97

11

-7

-4

Nets

2008-09

Conventional wisdom, and frankly my assumption before this project, was that teams tank mostly for the worst record in the league but according to this measure it’s not true. Worst record usually belongs to either actually really bad team or one which started tanking very early so other teams can’t catch it… and the real tank-fest goes on between 2nd worst and 7th worst team each year.
When you check example of odds for Top3 pick it makes a lot of sense. First of all, there are a lot of ties in that range and second of all, team can almost double it’s odds for Top3 by moving up just one spot!